Agentic AI Data Scientist/Engineer

Agentic AI Data Scientist/Engineer 1 Lead to head up the team responsible for designing, building, and deploying agents and multi-agent workflows on our client's Multi-Agentic Platform. You'll lead a group of engineers/data scientists across the full agent lifecycle orchestration, LLM and prompt engineering, system integration, and evaluation while staying hands-on with architecture and technical direction. This role sits at the intersection of applied AI engineering and data science, and requires someone who can both lead a team and dive deep into agent design, orchestration frameworks, and LLM behavior. Required Qualifications : - 8+ years in software/ML engineering or data science, including recent hands-on experience building LLM-powered or agentic systems in production. - Strong Python engineering skills production-quality code, testing, packaging, and API design. - Hands-on experience with agent orchestration frameworks, especially LangGraph (LangChain, AutoGen, CrewAI, or Semantic Kernel also relevant). - Deep understanding of LLM fundamentals: prompt engineering, context management, tool/function calling, RAG, embeddings, and model selection tradeoffs. - Experience designing and implementing evaluation frameworks for LLM/agent outputs (offline eval sets, human-in-the-loop review, automated scoring/LLM-as-judge techniques, regression testing). - Solid data science fundamentals statistics, experimentation, model evaluation methodology and ability to apply them to non-deterministic AI systems. - Prior experience leading a team of engineers or data scientists technical mentorship, code/design review, and delivery ownership. - Experience integrating AI systems with external APIs, databases, and enterprise data sources. - Strong communication skills able to explain agent architecture and tradeoffs to both technical teams and business stakeholders. Preferred Qualifications : - Experience with multiple LLM providers/APIs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI) and model routing/fallback strategies. - Experience with vector databases and RAG pipelines (e.g., Pinecone, OpenSearch, pgvector, FAISS). - Familiarity with AWS-based deployment of AI workloads (Lambda, ECS/EKS, SageMaker, Bedrock). - Experience building observability/tracing tooling for agentic systems (e.g., LangSmith, custom tracing, OpenTelemetry). - Background in consulting or client-facing delivery environments, managing scope and stakeholder expectations. - Experience with multi-agent design patterns (planner/executor, supervisor/worker, hierarchical agents, tool-routing agents). - Prior experience partnering with front-end/UI engineering teams to expose agent configuration and monitoring through a self-service interface. Agentic AI Data Scientist/Engineer 1 Lead to head up the team responsible for designing, building, and deploying agents and multi-agent workflows on our client's Multi-Agentic Platform. You'll lead a group of engineers/data scientists across the full agent lifecycle orchestration, LLM and prompt engineering, system integration, and evaluation while staying hands-on with architecture and technical direction. This role sits at the intersection of applied AI engineering and data science, and requires someone who can both lead a team and dive deep into agent design, orchestration frameworks, and LLM behavior. Required Qualifications : - 8+ years in software/ML engineering or data science, including recent hands-on experience building LLM-powered or agentic systems in production. - Strong Python engineering skills production-quality code, testing, packaging, and API design. - Hands-on experience with agent orchestration frameworks, especially LangGraph (LangChain, AutoGen, CrewAI, or Semantic Kernel also relevant). - Deep understanding of LLM fundamentals: prompt engineering, context management, tool/function calling, RAG, embeddings, and model selection tradeoffs. - Experience designing and implementing evaluation frameworks for LLM/agent outputs (offline eval sets, human-in-the-loop review, automated scoring/LLM-as-judge techniques, regression testing). - Solid data science fundamentals statistics, experimentation, model evaluation methodology and ability to apply them to non-deterministic AI systems. - Prior experience leading a team of engineers or data scientists technical mentorship, code/design review, and delivery ownership. - Experience integrating AI systems with external APIs, databases, and enterprise data sources. - Strong communication skills able to explain agent architecture and tradeoffs to both technical teams and business stakeholders. Preferred Qualifications : - Experience with multiple LLM providers/APIs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI) and model routing/fallback strategies. - Experience with vector databases and RAG pipelines (e.g., Pinecone, OpenSearch, pgvector, FAISS). - Familiarity with AWS-based deployment of AI workloads (Lambda, EC